A recreation of https://gpflow.readthedocs.io/en/master/notebooks/advanced/gps_for_big_data.html

Regression: Sparse Variational Gaussian Process for Stochastic Optimisation with Flux.jl

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In this example, we show how to construct and train the stochastic variational Gaussian process (SVGP) model for efficient inference in large scale datasets. For a basic introduction to the functionality of this library, please refer to the User Guide.

Setup

using ApproximateGPs
using Distributions
using LinearAlgebra

using Plots
default(; palette=:seaborn_colorblind, legend=:outertopright, size=(700, 400))

using Random
Random.seed!(1234);

Generate some training data

We define a data-generating function g:

g(x) = sin(3π * x) + 0.3 * cos(9π * x) + 0.5 * sin(7π * x)

N = 10000 # Number of training points
x = rand(Uniform(-1, 1), N)
y = g.(x) + 0.3 * randn(N)

scatter(x, y; xlabel="x", ylabel="y", markershape=:xcross, markeralpha=0.1, legend=false)